基于遗传算法最小二乘支持向量机的耕地变化预测

Cultivated land change forecast based on genetic algorithm and least squares support vector machines

针对耕地变化内部规律与模拟方法进行研究,提出最小二乘支持向量机耕地变化预测方法,有效构建耕地变化与耕地变化影响因子之间复杂的非线性关系模型。利用遗传算法全局寻优功能优化最小二乘支持向量机内部参数,提高最小二乘支持向量机耕地变化预测模型精度。利用该模型对江苏无锡市1987-2000年期间耕地变化进行预测,并与多元回归、GM(1,1)、BP网络、支持向量机(SVM)耕地预测模型和实际调查耕地变化数据进行比较分析。预测精度评价结果证实,该方法耕地预测精度远高于多元回归、GM(1,1),BP网络模型,略高于SVM模型,但算法复杂度和计算效率远优于SVM预测模型,是一种有效的耕地变化预测方法。

A prediction method of cultivated land change based on least squares support vector machines（LS-SVM）was developed by studying the inherent tendency toward land change and simulating the trajectories of changes in land use.A nonlinear dynamic model of cultivated land change and influence factors was introduced.The prediction accuracy was improved by using the genetic algorithm to automatically determine the optimal parameters of least squares support vector machines.The proposed model has been thoroughly tested on predicting the cultivated land change during the period of 1987-2000 in Wuxi,Jiangsu.The results were compared and analyzed with those obtained from multiple regression,GM（1,1）,BP algorithm,support vector machines（SVM）and the survey data on cultivated land change.The evaluation of prediction precision showed that the method based on LS-SVM was far more accurate than multiple regression,GM（1,1）and BP network model.Compared with the support vector machines model,the method was even slightly better and possesses much less algorithm complexity and higher computational efficiency.The overall performance suggests that the method is effective in predicting land change.